Decision tree algorithm in deciding hospitalization for adult patients with dengue haemorrhagic fever in Singapore

To develop a simple decision tree for clinicians to decide between hospitalization and outpatient monitoring of adult dengue patients.

Retrospective cohort study on all laboratory-diagnosed dengue patients admitted in 2004 to Tan Tock Seng Hospital, Singapore. Demographic, clinical, laboratory and radiological data were collected, and cases classified as dengue fever (DF) or dengue haemorrhagic fever (DHF) using World Health Organization criteria. To develop the decision tree, we used chi-squared automatic interaction detector (CHAID) with bi-way and multi-way splitting. The resulting trees were pruned to achieve the highest sensitivity with the shortest tree.

In 2004, 1973 probable and confirmed adult dengue patients were admitted; DF comprised 1855 (94.0%) and DHF 118 (6.0%) of the cases. The best decision tree prediction had three branches, consisting of a history of clinical bleeding, serum urea, and serum total protein. This decision tree had a sensitivity of 1.00, specificity of 0.46, positive predictive value of 7.5%, and negative predictive value of 100%. The overall accuracy of the decision tree was 48.1%. The test sensitivity and specificity compared favourably with other predictive probability equations and sophisticated laboratory tests, and would prevent 43.9% of mild DF cases from hospitalization.

A simple decision tree is effective in predicting DHF in the clinical setting for adult dengue infection.